In this study, the Regional Climate Model version 4 (RegCM4) coupled with the Community Land Model version 4.5 (CLM45) including a module of carbon–nitrogen cycling (CN) (RegCM4-CLM45-CN) is used to examine the sensitivity of the terrestrial carbon fluxes of Africa to leaf area index (LAI) parameterization. Two LAI formulas were implemented in CLM45-CN. The new LAI formula is based on a modified BioGeochemical Cycles (BIOME-BGC) ecosystem model. The two simulations were designated as LAIorg and LAImod, respectively and they both shared the same initial and lateral boundary conditions and they were evaluated concerning reanalysis products and FLUXNET measurements. In LAIorg, the above-ground terrestrial carbon fluxes were overestimated to the reanalysis products, which were also noted for the below-ground terrestrial fluxes. On the other-hand in LAImod, terrestrial carbon fluxes were notably decreased relative to LAIorg and the model bias was reduced. For in-situ observation, LAImod was better matched to the observation than LAIorg although both were limited in capturing the observed magnitude and seasonality of gross primary production (GPP) to some extent. In conclusion, switching between the two formulas has a substantial effect on the simulated terrestrial carbon fluxes. Despite noted biases, the regional coupled RegCM4-CLM4-CN-LAImod model can be recommended for future studies to investigate influence of climate change on the terrestrial carbon fluxes of Africa.
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On the role of Leaf Area Index parameterization in simulating the terrestrial carbon fluxes of Africa using a regional coupled climate-vegetation model
Published:
09 December 2022
by MDPI
in The 3rd International Electronic Conference on Applied Sciences
session Environmental and Earth Sciences
Abstract:
Keywords: Africa; Community land model; Leaf area index; Regional climate model; Terrestrial carbon fluxes
Comments on this paper
Seemant Tiwari
15 December 2022
The proposed research is extremely well organized, the topics are apparent, and the writing is brief and persuasive.
Samy Anwar
15 December 2022
Many thanks for your kind words.
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